Overview

Brought to you by YData

Dataset statistics

Number of variables31
Number of observations899164
Missing cells1016326
Missing cells (%)3.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory209.2 MiB
Average record size in memory244.0 B

Variable types

Numeric14
Text5
DateTime3
Categorical7
Boolean2

Alerts

ApprovalFY is highly overall correlated with RetainedJob and 3 other fieldsHigh correlation
DisbursementGross is highly overall correlated with GrAppv and 2 other fieldsHigh correlation
GrAppv is highly overall correlated with DisbursementGross and 2 other fieldsHigh correlation
MIS_Status is highly overall correlated with has_chgoff_dateHigh correlation
RetainedJob is highly overall correlated with ApprovalFYHigh correlation
RevLineCr is highly overall correlated with ApprovalFY and 2 other fieldsHigh correlation
SBA_Appv is highly overall correlated with DisbursementGross and 2 other fieldsHigh correlation
Term is highly overall correlated with DisbursementGross and 3 other fieldsHigh correlation
UrbanRural is highly overall correlated with ApprovalFY and 2 other fieldsHigh correlation
bool_RetainedJob is highly overall correlated with ApprovalFY and 2 other fieldsHigh correlation
has_chgoff_date is highly overall correlated with MIS_Status and 1 other fieldsHigh correlation
Recession is highly imbalanced (97.4%) Imbalance
RevLineCr has 277479 (30.9%) missing values Missing
ChgOffDate has 736465 (81.9%) missing values Missing
NoEmp is highly skewed (γ1 = 80.24824355) Skewed
CreateJob is highly skewed (γ1 = 36.99135473) Skewed
RetainedJob is highly skewed (γ1 = 36.85481184) Skewed
BalanceGross is highly skewed (γ1 = 601.1741483) Skewed
LoanNr_ChkDgt has unique values Unique
NAICS has 201948 (22.5%) zeros Zeros
CreateJob has 629248 (70.0%) zeros Zeros
RetainedJob has 440403 (49.0%) zeros Zeros
FranchiseCode has 208835 (23.2%) zeros Zeros
BalanceGross has 899150 (> 99.9%) zeros Zeros
ChgOffPrinGr has 737152 (82.0%) zeros Zeros

Reproduction

Analysis started2025-02-13 10:01:57.385466
Analysis finished2025-02-13 10:03:16.162326
Duration1 minute and 18.78 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

LoanNr_ChkDgt
Real number (ℝ)

Unique 

Distinct899164
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7726123 × 109
Minimum1.000014 × 109
Maximum9.996003 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2025-02-13T11:03:16.224007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.000014 × 109
5-th percentile1.3484572 × 109
Q12.5897575 × 109
median4.361439 × 109
Q36.9046265 × 109
95-th percentile9.1648039 × 109
Maximum9.996003 × 109
Range8.995989 × 109
Interquartile range (IQR)4.314869 × 109

Descriptive statistics

Standard deviation2.538175 × 109
Coefficient of variation (CV)0.53182091
Kurtosis-1.086499
Mean4.7726123 × 109
Median Absolute Deviation (MAD)2.0134 × 109
Skewness0.3647571
Sum4.2913612 × 1015
Variance6.4423325 × 1018
MonotonicityStrictly increasing
2025-02-13T11:03:16.314362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000014003 1
 
< 0.1%
5944984007 1
 
< 0.1%
5944874009 1
 
< 0.1%
5944884001 1
 
< 0.1%
5944904005 1
 
< 0.1%
5944914008 1
 
< 0.1%
5944924000 1
 
< 0.1%
5944934003 1
 
< 0.1%
5944944006 1
 
< 0.1%
5944954009 1
 
< 0.1%
Other values (899154) 899154
> 99.9%
ValueCountFrequency (%)
1000014003 1
< 0.1%
1000024006 1
< 0.1%
1000034009 1
< 0.1%
1000044001 1
< 0.1%
1000054004 1
< 0.1%
1000084002 1
< 0.1%
1000093009 1
< 0.1%
1000094005 1
< 0.1%
1000104006 1
< 0.1%
1000124001 1
< 0.1%
ValueCountFrequency (%)
9996003010 1
< 0.1%
9995973006 1
< 0.1%
9995613003 1
< 0.1%
9995603000 1
< 0.1%
9995573004 1
< 0.1%
9995563001 1
< 0.1%
9995493004 1
< 0.1%
9995473009 1
< 0.1%
9995453003 1
< 0.1%
9995423005 1
< 0.1%

Name
Text

Distinct779583
Distinct (%)86.7%
Missing14
Missing (%)< 0.1%
Memory size6.9 MiB
2025-02-13T11:03:16.700341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length30
Median length23
Mean length21.775963
Min length1

Characters and Unicode

Total characters19579857
Distinct characters91
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique706468 ?
Unique (%)78.6%

Sample

1st rowABC HOBBYCRAFT
2nd rowLANDMARK BAR & GRILLE (THE)
3rd rowWHITLOCK DDS, TODD M.
4th rowBIG BUCKS PAWN & JEWELRY, LLC
5th rowANASTASIA CONFECTIONS, INC.
ValueCountFrequency (%)
inc 263379
 
8.4%
100280
 
3.2%
llc 77826
 
2.5%
and 28959
 
0.9%
the 28389
 
0.9%
of 23026
 
0.7%
dba 20214
 
0.6%
co 18216
 
0.6%
a 18114
 
0.6%
services 17318
 
0.6%
Other values (226643) 2530176
80.9%
2025-02-13T11:03:17.154859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2231639
 
11.4%
E 1354056
 
6.9%
I 1226719
 
6.3%
A 1177821
 
6.0%
N 1170319
 
6.0%
R 1052562
 
5.4%
C 1038114
 
5.3%
S 1009495
 
5.2%
O 933206
 
4.8%
T 917437
 
4.7%
Other values (81) 7468489
38.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19579857
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2231639
 
11.4%
E 1354056
 
6.9%
I 1226719
 
6.3%
A 1177821
 
6.0%
N 1170319
 
6.0%
R 1052562
 
5.4%
C 1038114
 
5.3%
S 1009495
 
5.2%
O 933206
 
4.8%
T 917437
 
4.7%
Other values (81) 7468489
38.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19579857
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2231639
 
11.4%
E 1354056
 
6.9%
I 1226719
 
6.3%
A 1177821
 
6.0%
N 1170319
 
6.0%
R 1052562
 
5.4%
C 1038114
 
5.3%
S 1009495
 
5.2%
O 933206
 
4.8%
T 917437
 
4.7%
Other values (81) 7468489
38.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19579857
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2231639
 
11.4%
E 1354056
 
6.9%
I 1226719
 
6.3%
A 1177821
 
6.0%
N 1170319
 
6.0%
R 1052562
 
5.4%
C 1038114
 
5.3%
S 1009495
 
5.2%
O 933206
 
4.8%
T 917437
 
4.7%
Other values (81) 7468489
38.1%

City
Text

Distinct32581
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
2025-02-13T11:03:17.312126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length30
Median length27
Mean length9.1030491
Min length1

Characters and Unicode

Total characters8185134
Distinct characters80
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12872 ?
Unique (%)1.4%

Sample

1st rowEVANSVILLE
2nd rowNEW PARIS
3rd rowBLOOMINGTON
4th rowBROKEN ARROW
5th rowORLANDO
ValueCountFrequency (%)
city 23834
 
2.0%
san 21949
 
1.8%
new 16076
 
1.3%
los 13000
 
1.1%
angeles 12380
 
1.0%
lake 10730
 
0.9%
houston 10587
 
0.9%
beach 10462
 
0.9%
park 10316
 
0.9%
york 9724
 
0.8%
Other values (17695) 1066619
88.5%
2025-02-13T11:03:17.556560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 744425
 
9.1%
E 723116
 
8.8%
O 632534
 
7.7%
N 621363
 
7.6%
L 573594
 
7.0%
R 513616
 
6.3%
S 475419
 
5.8%
I 468368
 
5.7%
T 425126
 
5.2%
306954
 
3.8%
Other values (70) 2700619
33.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8185134
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 744425
 
9.1%
E 723116
 
8.8%
O 632534
 
7.7%
N 621363
 
7.6%
L 573594
 
7.0%
R 513616
 
6.3%
S 475419
 
5.8%
I 468368
 
5.7%
T 425126
 
5.2%
306954
 
3.8%
Other values (70) 2700619
33.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8185134
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 744425
 
9.1%
E 723116
 
8.8%
O 632534
 
7.7%
N 621363
 
7.6%
L 573594
 
7.0%
R 513616
 
6.3%
S 475419
 
5.8%
I 468368
 
5.7%
T 425126
 
5.2%
306954
 
3.8%
Other values (70) 2700619
33.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8185134
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 744425
 
9.1%
E 723116
 
8.8%
O 632534
 
7.7%
N 621363
 
7.6%
L 573594
 
7.0%
R 513616
 
6.3%
S 475419
 
5.8%
I 468368
 
5.7%
T 425126
 
5.2%
306954
 
3.8%
Other values (70) 2700619
33.0%

State
Text

Distinct51
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
2025-02-13T11:03:17.643818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1798328
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIN
2nd rowIN
3rd rowIN
4th rowOK
5th rowFL
ValueCountFrequency (%)
ca 130622
 
14.5%
tx 70462
 
7.8%
ny 57693
 
6.4%
fl 41213
 
4.6%
pa 35170
 
3.9%
oh 32622
 
3.6%
il 29669
 
3.3%
ma 25272
 
2.8%
mn 24373
 
2.7%
nj 24036
 
2.7%
Other values (41) 428032
47.6%
2025-02-13T11:03:17.781690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 306179
17.0%
C 184960
10.3%
N 181728
10.1%
M 132550
 
7.4%
T 125074
 
7.0%
I 119520
 
6.6%
O 94907
 
5.3%
L 88820
 
4.9%
X 70462
 
3.9%
Y 68255
 
3.8%
Other values (14) 425873
23.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1798328
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 306179
17.0%
C 184960
10.3%
N 181728
10.1%
M 132550
 
7.4%
T 125074
 
7.0%
I 119520
 
6.6%
O 94907
 
5.3%
L 88820
 
4.9%
X 70462
 
3.9%
Y 68255
 
3.8%
Other values (14) 425873
23.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1798328
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 306179
17.0%
C 184960
10.3%
N 181728
10.1%
M 132550
 
7.4%
T 125074
 
7.0%
I 119520
 
6.6%
O 94907
 
5.3%
L 88820
 
4.9%
X 70462
 
3.9%
Y 68255
 
3.8%
Other values (14) 425873
23.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1798328
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 306179
17.0%
C 184960
10.3%
N 181728
10.1%
M 132550
 
7.4%
T 125074
 
7.0%
I 119520
 
6.6%
O 94907
 
5.3%
L 88820
 
4.9%
X 70462
 
3.9%
Y 68255
 
3.8%
Other values (14) 425873
23.7%

Zip
Real number (ℝ)

Distinct33611
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53804.391
Minimum0
Maximum99999
Zeros283
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2025-02-13T11:03:17.852681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3838
Q127587
median55410
Q383704
95-th percentile95822
Maximum99999
Range99999
Interquartile range (IQR)56117

Descriptive statistics

Standard deviation31184.159
Coefficient of variation (CV)0.5795839
Kurtosis-1.3359893
Mean53804.391
Median Absolute Deviation (MAD)28206
Skewness-0.16816663
Sum4.8378972 × 1010
Variance9.7245178 × 108
MonotonicityNot monotonic
2025-02-13T11:03:17.933512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10001 933
 
0.1%
90015 926
 
0.1%
93401 806
 
0.1%
90010 733
 
0.1%
33166 671
 
0.1%
90021 666
 
0.1%
59601 640
 
0.1%
65804 599
 
0.1%
3801 581
 
0.1%
59101 578
 
0.1%
Other values (33601) 892031
99.2%
ValueCountFrequency (%)
0 283
< 0.1%
1 24
 
< 0.1%
2 11
 
< 0.1%
3 5
 
< 0.1%
4 5
 
< 0.1%
5 5
 
< 0.1%
6 4
 
< 0.1%
7 6
 
< 0.1%
8 15
 
< 0.1%
9 24
 
< 0.1%
ValueCountFrequency (%)
99999 209
< 0.1%
99950 3
 
< 0.1%
99929 15
 
< 0.1%
99928 1
 
< 0.1%
99926 1
 
< 0.1%
99925 4
 
< 0.1%
99923 1
 
< 0.1%
99921 13
 
< 0.1%
99919 2
 
< 0.1%
99918 1
 
< 0.1%

Bank
Text

Distinct5803
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
2025-02-13T11:03:18.064272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length30
Median length26
Mean length23.159879
Min length3

Characters and Unicode

Total characters20824529
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique923 ?
Unique (%)0.1%

Sample

1st rowFIFTH THIRD BANK
2nd row1ST SOURCE BANK
3rd rowGRANT COUNTY STATE BANK
4th row1ST NATL BK & TR CO OF BROKEN
5th rowFLORIDA BUS. DEVEL CORP
ValueCountFrequency (%)
bank 651608
18.5%
natl 318240
 
9.0%
assoc 306768
 
8.7%
of 142852
 
4.1%
national 125899
 
3.6%
america 100686
 
2.9%
association 84965
 
2.4%
fargo 63732
 
1.8%
wells 63650
 
1.8%
52264
 
1.5%
Other values (3603) 1608268
45.7%
2025-02-13T11:03:18.284059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 2762231
13.3%
2620014
12.6%
N 2105500
10.1%
S 1520499
 
7.3%
O 1336993
 
6.4%
T 1181841
 
5.7%
C 1134642
 
5.4%
I 1061717
 
5.1%
E 923739
 
4.4%
L 922583
 
4.4%
Other values (44) 5254770
25.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20824529
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 2762231
13.3%
2620014
12.6%
N 2105500
10.1%
S 1520499
 
7.3%
O 1336993
 
6.4%
T 1181841
 
5.7%
C 1134642
 
5.4%
I 1061717
 
5.1%
E 923739
 
4.4%
L 922583
 
4.4%
Other values (44) 5254770
25.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20824529
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 2762231
13.3%
2620014
12.6%
N 2105500
10.1%
S 1520499
 
7.3%
O 1336993
 
6.4%
T 1181841
 
5.7%
C 1134642
 
5.4%
I 1061717
 
5.1%
E 923739
 
4.4%
L 922583
 
4.4%
Other values (44) 5254770
25.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20824529
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 2762231
13.3%
2620014
12.6%
N 2105500
10.1%
S 1520499
 
7.3%
O 1336993
 
6.4%
T 1181841
 
5.7%
C 1134642
 
5.4%
I 1061717
 
5.1%
E 923739
 
4.4%
L 922583
 
4.4%
Other values (44) 5254770
25.2%
Distinct56
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
2025-02-13T11:03:18.373829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1798328
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowOH
2nd rowIN
3rd rowIN
4th rowOK
5th rowFL
ValueCountFrequency (%)
ca 118255
 
13.2%
nc 79526
 
8.8%
il 66006
 
7.3%
oh 58498
 
6.5%
sd 51105
 
5.7%
tx 48069
 
5.3%
ri 45385
 
5.0%
ny 39661
 
4.4%
va 29007
 
3.2%
de 24541
 
2.7%
Other values (46) 339111
37.7%
2025-02-13T11:03:18.521488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 241894
13.5%
C 229804
12.8%
N 187930
10.5%
I 159224
 
8.9%
O 102724
 
5.7%
L 97096
 
5.4%
D 96248
 
5.4%
T 95265
 
5.3%
M 85204
 
4.7%
S 73429
 
4.1%
Other values (14) 429510
23.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1798328
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 241894
13.5%
C 229804
12.8%
N 187930
10.5%
I 159224
 
8.9%
O 102724
 
5.7%
L 97096
 
5.4%
D 96248
 
5.4%
T 95265
 
5.3%
M 85204
 
4.7%
S 73429
 
4.1%
Other values (14) 429510
23.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1798328
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 241894
13.5%
C 229804
12.8%
N 187930
10.5%
I 159224
 
8.9%
O 102724
 
5.7%
L 97096
 
5.4%
D 96248
 
5.4%
T 95265
 
5.3%
M 85204
 
4.7%
S 73429
 
4.1%
Other values (14) 429510
23.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1798328
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 241894
13.5%
C 229804
12.8%
N 187930
10.5%
I 159224
 
8.9%
O 102724
 
5.7%
L 97096
 
5.4%
D 96248
 
5.4%
T 95265
 
5.3%
M 85204
 
4.7%
S 73429
 
4.1%
Other values (14) 429510
23.9%

NAICS
Real number (ℝ)

Zeros 

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.612263
Minimum0
Maximum92
Zeros201948
Zeros (%)22.5%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2025-02-13T11:03:18.585643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q123
median44
Q356
95-th percentile81
Maximum92
Range92
Interquartile range (IQR)33

Descriptive statistics

Standard deviation26.284706
Coefficient of variation (CV)0.66354972
Kurtosis-1.0572678
Mean39.612263
Median Absolute Deviation (MAD)18
Skewness-0.24819754
Sum35617921
Variance690.88577
MonotonicityNot monotonic
2025-02-13T11:03:18.648089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 201948
22.5%
44 84737
9.4%
81 72618
 
8.1%
54 68170
 
7.6%
72 67600
 
7.5%
23 66646
 
7.4%
62 55366
 
6.2%
42 48743
 
5.4%
45 42514
 
4.7%
33 38284
 
4.3%
Other values (15) 152538
17.0%
ValueCountFrequency (%)
0 201948
22.5%
11 9005
 
1.0%
21 1851
 
0.2%
22 663
 
0.1%
23 66646
 
7.4%
31 11809
 
1.3%
32 17936
 
2.0%
33 38284
 
4.3%
42 48743
 
5.4%
44 84737
9.4%
ValueCountFrequency (%)
92 229
 
< 0.1%
81 72618
8.1%
72 67600
7.5%
71 14640
 
1.6%
62 55366
6.2%
61 6425
 
0.7%
56 32685
3.6%
55 257
 
< 0.1%
54 68170
7.6%
53 13632
 
1.5%
Distinct9859
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
Minimum1961-12-07 00:00:00
Maximum2014-06-25 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-13T11:03:18.972088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:19.055789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

ApprovalFY
Real number (ℝ)

High correlation 

Distinct51
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2001.1436
Minimum1962
Maximum2014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2025-02-13T11:03:19.146272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1962
5-th percentile1991
Q11997
median2002
Q32006
95-th percentile2009
Maximum2014
Range52
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.9138459
Coefficient of variation (CV)0.0029552332
Kurtosis-0.092531047
Mean2001.1436
Median Absolute Deviation (MAD)4
Skewness-0.58537855
Sum1.7993562 × 109
Variance34.973573
MonotonicityNot monotonic
2025-02-13T11:03:19.254112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2005 77525
 
8.6%
2006 76040
 
8.5%
2007 71876
 
8.0%
2004 68290
 
7.6%
2003 58193
 
6.5%
1995 45758
 
5.1%
2002 44391
 
4.9%
1996 40112
 
4.5%
2008 39540
 
4.4%
1997 37748
 
4.2%
Other values (41) 339691
37.8%
ValueCountFrequency (%)
1962 1
 
< 0.1%
1965 1
 
< 0.1%
1966 1
 
< 0.1%
1967 2
 
< 0.1%
1968 2
 
< 0.1%
1969 4
 
< 0.1%
1970 8
 
< 0.1%
1971 20
 
< 0.1%
1972 27
< 0.1%
1973 52
< 0.1%
ValueCountFrequency (%)
2014 268
 
< 0.1%
2013 2458
 
0.3%
2012 5997
 
0.7%
2011 12608
 
1.4%
2010 16848
 
1.9%
2009 19126
 
2.1%
2008 39540
4.4%
2007 71876
8.0%
2006 76040
8.5%
2005 77525
8.6%

Term
Real number (ℝ)

High correlation 

Distinct412
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110.77308
Minimum0
Maximum569
Zeros810
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2025-02-13T11:03:19.338599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16
Q160
median84
Q3120
95-th percentile300
Maximum569
Range569
Interquartile range (IQR)60

Descriptive statistics

Standard deviation78.857305
Coefficient of variation (CV)0.7118815
Kurtosis0.18570424
Mean110.77308
Median Absolute Deviation (MAD)33
Skewness1.1209258
Sum99603164
Variance6218.4746
MonotonicityNot monotonic
2025-02-13T11:03:19.424504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
84 230162
25.6%
60 89945
 
10.0%
240 85982
 
9.6%
120 77654
 
8.6%
300 44727
 
5.0%
180 28164
 
3.1%
36 19800
 
2.2%
12 17095
 
1.9%
48 15621
 
1.7%
72 9419
 
1.0%
Other values (402) 280595
31.2%
ValueCountFrequency (%)
0 810
 
0.1%
1 1608
0.2%
2 1809
0.2%
3 2112
0.2%
4 2173
0.2%
5 1866
0.2%
6 3054
0.3%
7 1761
0.2%
8 1693
0.2%
9 1875
0.2%
ValueCountFrequency (%)
569 1
< 0.1%
527 1
< 0.1%
511 1
< 0.1%
505 1
< 0.1%
481 1
< 0.1%
480 1
< 0.1%
461 1
< 0.1%
449 1
< 0.1%
445 1
< 0.1%
443 1
< 0.1%

NoEmp
Real number (ℝ)

Skewed 

Distinct599
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.411353
Minimum0
Maximum9999
Zeros6631
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2025-02-13T11:03:19.508687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q310
95-th percentile40
Maximum9999
Range9999
Interquartile range (IQR)8

Descriptive statistics

Standard deviation74.108196
Coefficient of variation (CV)6.4942514
Kurtosis7965.2886
Mean11.411353
Median Absolute Deviation (MAD)3
Skewness80.248244
Sum10260678
Variance5492.0248
MonotonicityNot monotonic
2025-02-13T11:03:19.593321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 154254
17.2%
2 138297
15.4%
3 90674
10.1%
4 73644
 
8.2%
5 60319
 
6.7%
6 45759
 
5.1%
10 31536
 
3.5%
7 31495
 
3.5%
8 31361
 
3.5%
12 20822
 
2.3%
Other values (589) 221003
24.6%
ValueCountFrequency (%)
0 6631
 
0.7%
1 154254
17.2%
2 138297
15.4%
3 90674
10.1%
4 73644
8.2%
5 60319
 
6.7%
6 45759
 
5.1%
7 31495
 
3.5%
8 31361
 
3.5%
9 18131
 
2.0%
ValueCountFrequency (%)
9999 4
< 0.1%
9992 1
 
< 0.1%
9945 1
 
< 0.1%
9090 1
 
< 0.1%
9000 2
 
< 0.1%
8500 1
 
< 0.1%
8041 1
 
< 0.1%
8018 1
 
< 0.1%
8000 7
< 0.1%
7999 1
 
< 0.1%

NewExist
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
1.0
645005 
0.0
254159 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2697492
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 645005
71.7%
0.0 254159
 
28.3%

Length

2025-02-13T11:03:19.664844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-13T11:03:19.706590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 645005
71.7%
0.0 254159
 
28.3%

Most occurring characters

ValueCountFrequency (%)
0 1153323
42.8%
. 899164
33.3%
1 645005
23.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2697492
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1153323
42.8%
. 899164
33.3%
1 645005
23.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2697492
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1153323
42.8%
. 899164
33.3%
1 645005
23.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2697492
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1153323
42.8%
. 899164
33.3%
1 645005
23.9%

CreateJob
Real number (ℝ)

Skewed  Zeros 

Distinct246
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.4303764
Minimum0
Maximum8800
Zeros629248
Zeros (%)70.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2025-02-13T11:03:19.766513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile10
Maximum8800
Range8800
Interquartile range (IQR)1

Descriptive statistics

Standard deviation236.68817
Coefficient of variation (CV)28.075634
Kurtosis1369.911
Mean8.4303764
Median Absolute Deviation (MAD)0
Skewness36.991355
Sum7580291
Variance56021.288
MonotonicityNot monotonic
2025-02-13T11:03:19.850364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 629248
70.0%
1 63174
 
7.0%
2 57831
 
6.4%
3 28806
 
3.2%
4 20511
 
2.3%
5 18691
 
2.1%
10 11602
 
1.3%
6 11009
 
1.2%
8 7378
 
0.8%
7 6374
 
0.7%
Other values (236) 44540
 
5.0%
ValueCountFrequency (%)
0 629248
70.0%
1 63174
 
7.0%
2 57831
 
6.4%
3 28806
 
3.2%
4 20511
 
2.3%
5 18691
 
2.1%
6 11009
 
1.2%
7 6374
 
0.7%
8 7378
 
0.8%
9 3330
 
0.4%
ValueCountFrequency (%)
8800 648
0.1%
5621 1
 
< 0.1%
5199 1
 
< 0.1%
5085 1
 
< 0.1%
3500 1
 
< 0.1%
3100 1
 
< 0.1%
3000 4
 
< 0.1%
2515 1
 
< 0.1%
2140 1
 
< 0.1%
2020 1
 
< 0.1%

RetainedJob
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct358
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.797257
Minimum0
Maximum9500
Zeros440403
Zeros (%)49.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2025-02-13T11:03:19.929092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile20
Maximum9500
Range9500
Interquartile range (IQR)4

Descriptive statistics

Standard deviation237.1206
Coefficient of variation (CV)21.961188
Kurtosis1362.0182
Mean10.797257
Median Absolute Deviation (MAD)1
Skewness36.854812
Sum9708505
Variance56226.179
MonotonicityNot monotonic
2025-02-13T11:03:20.006662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 440403
49.0%
1 88790
 
9.9%
2 76851
 
8.5%
3 49963
 
5.6%
4 39666
 
4.4%
5 32627
 
3.6%
6 23796
 
2.6%
7 16530
 
1.8%
8 15698
 
1.7%
10 15438
 
1.7%
Other values (348) 99402
 
11.1%
ValueCountFrequency (%)
0 440403
49.0%
1 88790
 
9.9%
2 76851
 
8.5%
3 49963
 
5.6%
4 39666
 
4.4%
5 32627
 
3.6%
6 23796
 
2.6%
7 16530
 
1.8%
8 15698
 
1.7%
9 8735
 
1.0%
ValueCountFrequency (%)
9500 1
 
< 0.1%
8800 648
0.1%
7250 1
 
< 0.1%
5000 1
 
< 0.1%
4441 1
 
< 0.1%
4000 2
 
< 0.1%
3900 1
 
< 0.1%
3860 1
 
< 0.1%
3225 1
 
< 0.1%
3200 1
 
< 0.1%

FranchiseCode
Real number (ℝ)

Zeros 

Distinct2768
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2753.7259
Minimum0
Maximum99999
Zeros208835
Zeros (%)23.2%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2025-02-13T11:03:20.092341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile15805
Maximum99999
Range99999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation12758.019
Coefficient of variation (CV)4.6330025
Kurtosis24.409524
Mean2753.7259
Median Absolute Deviation (MAD)0
Skewness4.9752152
Sum2.4760512 × 109
Variance1.6276705 × 108
MonotonicityNot monotonic
2025-02-13T11:03:20.176893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 638554
71.0%
0 208835
 
23.2%
78760 3373
 
0.4%
68020 1921
 
0.2%
50564 1034
 
0.1%
21780 1003
 
0.1%
25650 715
 
0.1%
79140 659
 
0.1%
22470 615
 
0.1%
17998 606
 
0.1%
Other values (2758) 41849
 
4.7%
ValueCountFrequency (%)
0 208835
 
23.2%
1 638554
71.0%
3 12
 
< 0.1%
395 5
 
< 0.1%
399 3
 
< 0.1%
400 2
 
< 0.1%
401 12
 
< 0.1%
404 1
 
< 0.1%
407 34
 
< 0.1%
414 2
 
< 0.1%
ValueCountFrequency (%)
99999 1
 
< 0.1%
92006 4
 
< 0.1%
92000 9
< 0.1%
91999 11
< 0.1%
91450 2
 
< 0.1%
91446 1
 
< 0.1%
91443 2
 
< 0.1%
91435 1
 
< 0.1%
91424 1
 
< 0.1%
91423 2
 
< 0.1%

UrbanRural
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
1
470654 
0
323167 
2
105343 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters899164
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 470654
52.3%
0 323167
35.9%
2 105343
 
11.7%

Length

2025-02-13T11:03:20.246007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-13T11:03:20.287565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 470654
52.3%
0 323167
35.9%
2 105343
 
11.7%

Most occurring characters

ValueCountFrequency (%)
1 470654
52.3%
0 323167
35.9%
2 105343
 
11.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 899164
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 470654
52.3%
0 323167
35.9%
2 105343
 
11.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 899164
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 470654
52.3%
0 323167
35.9%
2 105343
 
11.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 899164
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 470654
52.3%
0 323167
35.9%
2 105343
 
11.7%

RevLineCr
Boolean

High correlation  Missing 

Distinct2
Distinct (%)< 0.1%
Missing277479
Missing (%)30.9%
Memory size1.7 MiB
False
420288 
True
201397 
(Missing)
277479 
ValueCountFrequency (%)
False 420288
46.7%
True 201397
22.4%
(Missing) 277479
30.9%
2025-02-13T11:03:20.320449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

LowDoc
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size878.2 KiB
False
785252 
True
113912 
ValueCountFrequency (%)
False 785252
87.3%
True 113912
 
12.7%
2025-02-13T11:03:20.351190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

ChgOffDate
Date

Missing 

Distinct6448
Distinct (%)4.0%
Missing736465
Missing (%)81.9%
Memory size6.9 MiB
Minimum1988-10-03 00:00:00
Maximum2026-10-22 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-13T11:03:20.409334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:20.498528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct8472
Distinct (%)0.9%
Missing2368
Missing (%)0.3%
Memory size6.9 MiB
Minimum1975-01-17 00:00:00
Maximum2074-12-04 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-13T11:03:20.582658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:20.667308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

DisbursementGross
Real number (ℝ)

High correlation 

Distinct118859
Distinct (%)13.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201154.02
Minimum0
Maximum11446325
Zeros196
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2025-02-13T11:03:20.752207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10000
Q142000
median100000
Q3238000
95-th percentile761892.5
Maximum11446325
Range11446325
Interquartile range (IQR)196000

Descriptive statistics

Standard deviation287640.85
Coefficient of variation (CV)1.4299533
Kurtosis35.088599
Mean201154.02
Median Absolute Deviation (MAD)70000
Skewness3.9409921
Sum1.8087045 × 1011
Variance8.2737259 × 1010
MonotonicityNot monotonic
2025-02-13T11:03:20.836589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50000 43787
 
4.9%
100000 36714
 
4.1%
25000 27387
 
3.0%
150000 23373
 
2.6%
10000 21328
 
2.4%
35000 14748
 
1.6%
5000 14193
 
1.6%
75000 13528
 
1.5%
20000 13462
 
1.5%
30000 12696
 
1.4%
Other values (118849) 677948
75.4%
ValueCountFrequency (%)
0 196
< 0.1%
1 11
 
< 0.1%
2 3
 
< 0.1%
3 3
 
< 0.1%
4 3
 
< 0.1%
5 2
 
< 0.1%
6 4
 
< 0.1%
7 3
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
11446325 1
< 0.1%
11000000 1
< 0.1%
10465000 1
< 0.1%
9284449 1
< 0.1%
8995000 1
< 0.1%
8607858 1
< 0.1%
8602584 1
< 0.1%
7853275 1
< 0.1%
7699233 1
< 0.1%
7573881 1
< 0.1%

BalanceGross
Real number (ℝ)

Skewed  Zeros 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.989349
Minimum0
Maximum996262
Zeros899150
Zeros (%)> 99.9%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2025-02-13T11:03:20.903776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum996262
Range996262
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1442.1619
Coefficient of variation (CV)482.43344
Kurtosis380418.84
Mean2.989349
Median Absolute Deviation (MAD)0
Skewness601.17415
Sum2687915
Variance2079831
MonotonicityNot monotonic
2025-02-13T11:03:20.960936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 899150
> 99.9%
12750 1
 
< 0.1%
827875 1
 
< 0.1%
25000 1
 
< 0.1%
37100 1
 
< 0.1%
43127 1
 
< 0.1%
84617 1
 
< 0.1%
1760 1
 
< 0.1%
115820 1
 
< 0.1%
996262 1
 
< 0.1%
Other values (5) 5
 
< 0.1%
ValueCountFrequency (%)
0 899150
> 99.9%
600 1
 
< 0.1%
1760 1
 
< 0.1%
9111 1
 
< 0.1%
12750 1
 
< 0.1%
25000 1
 
< 0.1%
37100 1
 
< 0.1%
41509 1
 
< 0.1%
43127 1
 
< 0.1%
84617 1
 
< 0.1%
ValueCountFrequency (%)
996262 1
< 0.1%
827875 1
< 0.1%
395476 1
< 0.1%
115820 1
< 0.1%
96908 1
< 0.1%
84617 1
< 0.1%
43127 1
< 0.1%
41509 1
< 0.1%
37100 1
< 0.1%
25000 1
< 0.1%

MIS_Status
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
P I F
741345 
CHGOFF
157819 

Length

Max length6
Median length5
Mean length5.1755175
Min length5

Characters and Unicode

Total characters4653639
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowP I F
2nd rowP I F
3rd rowP I F
4th rowP I F
5th rowP I F

Common Values

ValueCountFrequency (%)
P I F 741345
82.4%
CHGOFF 157819
 
17.6%

Length

2025-02-13T11:03:21.025815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-13T11:03:21.068727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
p 741345
31.1%
i 741345
31.1%
f 741345
31.1%
chgoff 157819
 
6.6%

Most occurring characters

ValueCountFrequency (%)
1482690
31.9%
F 1056983
22.7%
P 741345
15.9%
I 741345
15.9%
C 157819
 
3.4%
H 157819
 
3.4%
G 157819
 
3.4%
O 157819
 
3.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4653639
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1482690
31.9%
F 1056983
22.7%
P 741345
15.9%
I 741345
15.9%
C 157819
 
3.4%
H 157819
 
3.4%
G 157819
 
3.4%
O 157819
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4653639
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1482690
31.9%
F 1056983
22.7%
P 741345
15.9%
I 741345
15.9%
C 157819
 
3.4%
H 157819
 
3.4%
G 157819
 
3.4%
O 157819
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4653639
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1482690
31.9%
F 1056983
22.7%
P 741345
15.9%
I 741345
15.9%
C 157819
 
3.4%
H 157819
 
3.4%
G 157819
 
3.4%
O 157819
 
3.4%

ChgOffPrinGr
Real number (ℝ)

Zeros 

Distinct83165
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13503.295
Minimum0
Maximum3512596
Zeros737152
Zeros (%)82.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2025-02-13T11:03:21.128249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile64888.85
Maximum3512596
Range3512596
Interquartile range (IQR)0

Descriptive statistics

Standard deviation65152.293
Coefficient of variation (CV)4.8249181
Kurtosis184.31916
Mean13503.295
Median Absolute Deviation (MAD)0
Skewness11.22097
Sum1.2141677 × 1010
Variance4.2448212 × 109
MonotonicityNot monotonic
2025-02-13T11:03:21.211669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 737152
82.0%
50000 2110
 
0.2%
10000 1865
 
0.2%
25000 1371
 
0.2%
35000 1345
 
0.1%
100000 1028
 
0.1%
20000 594
 
0.1%
30000 492
 
0.1%
15000 467
 
0.1%
5000 356
 
< 0.1%
Other values (83155) 152384
 
16.9%
ValueCountFrequency (%)
0 737152
82.0%
1 6
 
< 0.1%
3 3
 
< 0.1%
4 2
 
< 0.1%
5 5
 
< 0.1%
6 3
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
11 3
 
< 0.1%
ValueCountFrequency (%)
3512596 1
< 0.1%
2223766 1
< 0.1%
2157499 1
< 0.1%
1999999 1
< 0.1%
1961398 1
< 0.1%
1933715 1
< 0.1%
1932180 1
< 0.1%
1931439 1
< 0.1%
1926148 1
< 0.1%
1917676 1
< 0.1%

GrAppv
Real number (ℝ)

High correlation 

Distinct22128
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean192686.98
Minimum200
Maximum5472000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2025-02-13T11:03:21.292389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum200
5-th percentile10000
Q135000
median90000
Q3225000
95-th percentile750000
Maximum5472000
Range5471800
Interquartile range (IQR)190000

Descriptive statistics

Standard deviation283263.39
Coefficient of variation (CV)1.4700702
Kurtosis21.018882
Mean192686.98
Median Absolute Deviation (MAD)65000
Skewness3.5207901
Sum1.7325719 × 1011
Variance8.0238149 × 1010
MonotonicityNot monotonic
2025-02-13T11:03:21.371372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50000 69394
 
7.7%
25000 51258
 
5.7%
100000 50977
 
5.7%
10000 38366
 
4.3%
150000 27624
 
3.1%
20000 23434
 
2.6%
35000 23181
 
2.6%
30000 21004
 
2.3%
5000 19146
 
2.1%
15000 18472
 
2.1%
Other values (22118) 556308
61.9%
ValueCountFrequency (%)
200 2
 
< 0.1%
300 1
 
< 0.1%
400 2
 
< 0.1%
500 33
 
< 0.1%
700 4
 
< 0.1%
800 4
 
< 0.1%
950 1
 
< 0.1%
1000 444
< 0.1%
1200 12
 
< 0.1%
1300 2
 
< 0.1%
ValueCountFrequency (%)
5472000 1
 
< 0.1%
5000000 40
< 0.1%
4991700 1
 
< 0.1%
4950000 1
 
< 0.1%
4908500 1
 
< 0.1%
4900000 2
 
< 0.1%
4872000 1
 
< 0.1%
4869000 1
 
< 0.1%
4830000 1
 
< 0.1%
4800000 1
 
< 0.1%

SBA_Appv
Real number (ℝ)

High correlation 

Distinct38326
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean149488.79
Minimum100
Maximum5472000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2025-02-13T11:03:21.452628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile5000
Q121250
median61250
Q3175000
95-th percentile626250
Maximum5472000
Range5471900
Interquartile range (IQR)153750

Descriptive statistics

Standard deviation228414.56
Coefficient of variation (CV)1.5279712
Kurtosis25.325514
Mean149488.79
Median Absolute Deviation (MAD)48750
Skewness3.6752753
Sum1.3441494 × 1011
Variance5.2173212 × 1010
MonotonicityNot monotonic
2025-02-13T11:03:21.534966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25000 49579
 
5.5%
12500 40147
 
4.5%
5000 31135
 
3.5%
50000 25047
 
2.8%
10000 17009
 
1.9%
17500 16141
 
1.8%
15000 14490
 
1.6%
7500 12781
 
1.4%
127500 11946
 
1.3%
80000 10965
 
1.2%
Other values (38316) 669924
74.5%
ValueCountFrequency (%)
100 2
 
< 0.1%
150 1
 
< 0.1%
200 2
 
< 0.1%
250 33
 
< 0.1%
350 4
 
< 0.1%
400 4
 
< 0.1%
475 1
 
< 0.1%
500 442
< 0.1%
600 12
 
< 0.1%
650 2
 
< 0.1%
ValueCountFrequency (%)
5472000 1
 
< 0.1%
5000000 1
 
< 0.1%
4869000 1
 
< 0.1%
4582000 1
 
< 0.1%
4500000 23
< 0.1%
4492530 1
 
< 0.1%
4410000 1
 
< 0.1%
4320000 1
 
< 0.1%
4050000 4
 
< 0.1%
4000000 13
< 0.1%

has_chgoff_date
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
0
736465 
1
162699 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters899164
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 736465
81.9%
1 162699
 
18.1%

Length

2025-02-13T11:03:21.608660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-13T11:03:21.646285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 736465
81.9%
1 162699
 
18.1%

Most occurring characters

ValueCountFrequency (%)
0 736465
81.9%
1 162699
 
18.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 899164
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 736465
81.9%
1 162699
 
18.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 899164
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 736465
81.9%
1 162699
 
18.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 899164
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 736465
81.9%
1 162699
 
18.1%

bool_RetainedJob
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
1
458761 
0
440403 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters899164
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 458761
51.0%
0 440403
49.0%

Length

2025-02-13T11:03:21.693571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-13T11:03:21.935596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 458761
51.0%
0 440403
49.0%

Most occurring characters

ValueCountFrequency (%)
1 458761
51.0%
0 440403
49.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 899164
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 458761
51.0%
0 440403
49.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 899164
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 458761
51.0%
0 440403
49.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 899164
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 458761
51.0%
0 440403
49.0%

bool_CreateJob
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
0
629248 
1
269916 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters899164
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 629248
70.0%
1 269916
30.0%

Length

2025-02-13T11:03:21.980211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-13T11:03:22.016524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 629248
70.0%
1 269916
30.0%

Most occurring characters

ValueCountFrequency (%)
0 629248
70.0%
1 269916
30.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 899164
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 629248
70.0%
1 269916
30.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 899164
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 629248
70.0%
1 269916
30.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 899164
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 629248
70.0%
1 269916
30.0%

Recession
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
1
896796 
0
 
2368

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters899164
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 896796
99.7%
0 2368
 
0.3%

Length

2025-02-13T11:03:22.064234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-13T11:03:22.099110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 896796
99.7%
0 2368
 
0.3%

Most occurring characters

ValueCountFrequency (%)
1 896796
99.7%
0 2368
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 899164
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 896796
99.7%
0 2368
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 899164
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 896796
99.7%
0 2368
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 899164
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 896796
99.7%
0 2368
 
0.3%

Interactions

2025-02-13T11:03:09.052795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:39.914691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:42.307491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:44.552475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:46.692244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:48.959307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:51.216421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:53.564845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:55.742148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:57.895116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:00.099189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:02.308974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:04.453883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:06.855071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:09.228055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:40.076365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:42.452632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:44.701065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:46.863273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:49.139521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:51.366151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:53.723616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:55.916856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:58.063068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:00.261973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:02.447260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:04.603818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:07.023557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:09.371119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:40.237632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:42.595371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:44.856291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:47.034464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:49.300174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:51.508137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:53.890087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:56.072993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:58.225342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:00.408975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:02.589925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:04.763955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:07.182013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:09.511870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:40.563406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:42.765393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:45.017503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:47.191443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:49.443617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:51.653340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:54.048421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:56.227366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:58.368998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:00.550681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:02.749197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:04.926699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:07.332515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:09.659850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:40.709795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:42.946426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:45.177579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:47.356613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:49.585211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:51.816848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:54.214209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:56.374242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:58.511069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:00.706413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:02.914256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:05.282982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:07.475598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:09.824519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:40.878643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:43.117839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:45.328575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:47.501489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:49.749152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:51.974133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:54.359391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:56.511184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:58.661167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:00.873573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:03.082176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:05.427800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:07.619067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:09.989021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:41.045373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:43.287402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:45.464666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:47.653364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:49.912254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:52.136138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:54.496025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:56.658066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:58.832447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:01.044841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:03.245133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:05.572807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:07.783136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:10.160546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:41.215896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:43.438660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:45.607925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:47.816522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:50.076087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:52.299507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:54.640063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:56.815139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:58.996449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:01.208157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:03.386089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:05.731842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:07.941994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:10.313996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:41.364249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:43.589682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:45.765249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:47.979875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:50.246346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:52.437224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:54.807914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:56.971363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:59.152264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:01.355991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:03.525377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:05.902824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:08.110710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:10.457708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:41.506418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:43.754852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:45.932468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:48.153657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:50.403448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:52.585108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:54.969935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:57.128246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:59.308300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:01.500708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:03.674969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:06.073081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:08.275709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:10.609680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:41.654947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:43.930429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:46.091619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:48.313163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:50.549195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:52.751225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:55.132900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:57.284948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:59.454037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:01.649205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:03.829045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:06.242791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:08.421552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:10.769669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:41.821324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:44.096558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:46.254289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:48.458176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:50.703155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:52.914642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:55.292201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:57.426228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:59.601390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:01.814860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:03.993174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:06.387913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:08.567737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:10.935847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:41.987708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:44.263003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:46.400251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:48.612091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:50.874419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:53.276321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:55.437930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:57.566959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:59.765582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:01.981804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:04.162284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:06.537878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:08.723453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:11.097043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:42.146306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:44.410110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:46.537419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:48.781107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:51.038301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:53.414469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:55.577029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:57.719437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:02:59.936222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:02.153957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:04.314797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:06.687845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T11:03:08.886317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-02-13T11:03:22.148329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ApprovalFYBalanceGrossChgOffPrinGrCreateJobDisbursementGrossFranchiseCodeGrAppvLoanNr_ChkDgtLowDocMIS_StatusNAICSNewExistNoEmpRecessionRetainedJobRevLineCrSBA_AppvTermUrbanRuralZipbool_CreateJobbool_RetainedJobhas_chgoff_date
ApprovalFY1.0000.0030.2510.268-0.222-0.452-0.300-0.2780.3630.3270.4400.053-0.2260.0690.5460.545-0.366-0.2970.659-0.0380.3200.6530.334
BalanceGross0.0031.000-0.0020.0030.0020.0010.0010.0010.0000.0000.0030.002-0.0000.0000.0000.0000.001-0.0000.002-0.0000.0010.0000.000
ChgOffPrinGr0.251-0.0021.0000.055-0.127-0.206-0.161-0.1840.0320.1810.0920.006-0.1060.0000.1560.047-0.180-0.4040.019-0.0050.0080.0150.178
CreateJob0.2680.0030.0551.0000.110-0.0540.093-0.0310.0100.0120.1560.0020.0340.0000.3770.0220.0780.0820.0250.0260.0420.0260.012
DisbursementGross-0.2220.002-0.1270.1101.0000.2040.9650.1020.0520.031-0.1200.0210.4450.002-0.0700.0410.9360.5210.0400.1150.0350.0150.032
FranchiseCode-0.4520.001-0.206-0.0540.2041.0000.2590.3920.0350.022-0.0850.1390.1210.005-0.2630.0910.2850.1960.0130.0310.0350.0620.024
GrAppv-0.3000.001-0.1610.0930.9650.2591.0000.1390.1180.074-0.1420.0490.4550.010-0.1380.1720.9860.5580.0510.1190.0580.0630.077
LoanNr_ChkDgt-0.2780.001-0.184-0.0310.1020.3920.1391.0000.2400.237-0.0470.0850.0750.027-0.1420.2290.1690.1210.1890.0310.1300.2380.240
LowDoc0.3630.0000.0320.0100.0520.0350.1180.2401.0000.0770.1470.1640.0030.0150.0100.2180.0990.1710.2060.1450.2350.3700.079
MIS_Status0.3270.0000.1810.0120.0310.0220.0740.2370.0771.0000.1480.0190.0040.0000.0130.1300.0700.4910.2100.0800.0740.2040.981
NAICS0.4400.0030.0920.156-0.120-0.085-0.142-0.0470.1470.1481.0000.132-0.1510.0130.2680.383-0.169-0.0760.432-0.0330.2070.4310.151
NewExist0.0530.0020.0060.0020.0210.1390.0490.0850.1640.0190.1321.0000.0030.0000.0030.0930.0400.1220.0410.1220.0430.1100.020
NoEmp-0.226-0.000-0.1060.0340.4450.1210.4550.0750.0030.004-0.1510.0031.0000.0000.1240.0090.4490.2000.0100.0590.0050.0120.004
Recession0.0690.0000.0000.0000.0020.0050.0100.0270.0150.0000.0130.0000.0001.0000.0000.0550.0090.0270.0190.0100.0070.0230.000
RetainedJob0.5460.0000.1560.377-0.070-0.263-0.138-0.1420.0100.0130.2680.0030.1240.0001.0000.022-0.205-0.1570.025-0.0260.0410.0270.012
RevLineCr0.5450.0000.0470.0220.0410.0910.1720.2290.2180.1300.3830.0930.0090.0550.0221.0000.1450.4560.5330.1670.1380.5510.131
SBA_Appv-0.3660.001-0.1800.0780.9360.2850.9860.1690.0990.070-0.1690.0400.4490.009-0.2050.1451.0000.5890.0510.1310.0910.0450.073
Term-0.297-0.000-0.4040.0820.5210.1960.5580.1210.1710.491-0.0760.1220.2000.027-0.1570.4560.5891.0000.2070.1420.2590.2710.504
UrbanRural0.6590.0020.0190.0250.0400.0130.0510.1890.2060.2100.4320.0410.0100.0190.0250.5330.0510.2071.0000.1260.3030.6260.213
Zip-0.038-0.000-0.0050.0260.1150.0310.1190.0310.1450.080-0.0330.1220.0590.010-0.0260.1670.1310.1420.1261.0000.0720.1440.084
bool_CreateJob0.3200.0010.0080.0420.0350.0350.0580.1300.2350.0740.2070.0430.0050.0070.0410.1380.0910.2590.3030.0721.0000.3840.077
bool_RetainedJob0.6530.0000.0150.0260.0150.0620.0630.2380.3700.2040.4310.1100.0120.0230.0270.5510.0450.2710.6260.1440.3841.0000.209
has_chgoff_date0.3340.0000.1780.0120.0320.0240.0770.2400.0790.9810.1510.0200.0040.0000.0120.1310.0730.5040.2130.0840.0770.2091.000

Missing values

2025-02-13T11:03:11.437076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-13T11:03:12.671749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-02-13T11:03:14.939060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

LoanNr_ChkDgtNameCityStateZipBankBankStateNAICSApprovalDateApprovalFYTermNoEmpNewExistCreateJobRetainedJobFranchiseCodeUrbanRuralRevLineCrLowDocChgOffDateDisbursementDateDisbursementGrossBalanceGrossMIS_StatusChgOffPrinGrGrAppvSBA_Appvhas_chgoff_datebool_RetainedJobbool_CreateJobRecession
01000014003ABC HOBBYCRAFTEVANSVILLEIN47711FIFTH THIRD BANKOH451997-02-2819978440.00010NYNaT1999-02-2860000.00.0P I F0.060000.048000.00001
11000024006LANDMARK BAR & GRILLE (THE)NEW PARISIN465261ST SOURCE BANKIN721997-02-2819976020.00010NYNaT1997-05-3140000.00.0P I F0.040000.032000.00001
21000034009WHITLOCK DDS, TODD M.BLOOMINGTONIN47401GRANT COUNTY STATE BANKIN621997-02-28199718071.00010NNNaT1997-12-31287000.00.0P I F0.0287000.0215250.00001
31000044001BIG BUCKS PAWN & JEWELRY, LLCBROKEN ARROWOK740121ST NATL BK & TR CO OF BROKENOK01997-02-2819976021.00010NYNaT1997-06-3035000.00.0P I F0.035000.028000.00001
41000054004ANASTASIA CONFECTIONS, INC.ORLANDOFL32801FLORIDA BUS. DEVEL CORPFL01997-02-281997240141.07710NNNaT1997-05-14229000.00.0P I F0.0229000.0229000.00111
51000084002B&T SCREW MACHINE COMPANY, INCPLAINVILLECT6062TD BANK, NATIONAL ASSOCIATIONDE331997-02-281997120191.00010NNNaT1997-06-30517000.00.0P I F0.0517000.0387750.00001
61000093009MIDDLE ATLANTIC SPORTS CO INCUNIONNJ7083WELLS FARGO BANK NATL ASSOCSD01980-06-02198045450.00000NN1991-06-241980-07-22600000.00.0CHGOFF208959.0600000.0499998.01001
71000094005WEAVER PRODUCTSSUMMERFIELDFL34491REGIONS BANKAL811997-02-2819978410.00010NYNaT1998-06-3045000.00.0P I F0.045000.036000.00001
81000104006TURTLE BEACH INNPORT SAINT JOEFL32456CENTENNIAL BANKFL721997-02-28199729720.00010NNNaT1997-07-31305000.00.0P I F0.0305000.0228750.00001
91000124001INTEXT BUILDING SYS LLCGLASTONBURYCT6073WEBSTER BANK NATL ASSOCCT01997-02-2819978430.00010NYNaT1997-04-3070000.00.0P I F0.070000.056000.00001
LoanNr_ChkDgtNameCityStateZipBankBankStateNAICSApprovalDateApprovalFYTermNoEmpNewExistCreateJobRetainedJobFranchiseCodeUrbanRuralRevLineCrLowDocChgOffDateDisbursementDateDisbursementGrossBalanceGrossMIS_StatusChgOffPrinGrGrAppvSBA_Appvhas_chgoff_datebool_RetainedJobbool_CreateJobRecession
8991549995423005LITWIN LIVERY SERVICES, INC.CAMPBELLOH44405JPMORGAN CHASE BANK NATL ASSOCIL01997-02-2719976011.00010NaNNNaT1997-09-3010000.00.0P I F0.010000.05000.00001
8991559995453003FUTURE LEADERS CENTER, INC.SO. OZONE PARKNY11420FLUSHING BANKNY621997-02-27199718021.00010NaNNNaT1997-06-30123000.00.0P I F0.0128000.096000.00001
8991569995473009FABRICATORS STEEL, INC.BALTIMOREMD21224BANK OF AMERICA NATL ASSOCMD331997-02-27199760201.00010NaNNNaT1997-06-3050000.00.0P I F0.050000.025000.00001
8991579995493004PULLTARPS MFG.EL CAJONCA92020U.S. BANK NATIONAL ASSOCIATIONCA311997-02-27199736401.00010NNNaT1997-03-31200000.00.0P I F0.0200000.0150000.00001
8991589995563001SHADES WINDOW TINTING AUTO ALAIRVINGTX75062LOANS FROM OLD CLOSED LENDERSDC01997-02-2719978450.00010NYNaT1997-06-3079000.00.0P I F0.079000.063200.00001
8991599995573004FABRIC FARMSUPPER ARLINGTONOH43221JPMORGAN CHASE BANK NATL ASSOCIL451997-02-2719976061.00010NaNNNaT1997-09-3070000.00.0P I F0.070000.056000.00001
8991609995603000FABRIC FARMSCOLUMBUSOH43221JPMORGAN CHASE BANK NATL ASSOCIL451997-02-2719976061.00010YNNaT1997-10-3185000.00.0P I F0.085000.042500.00001
8991619995613003RADCO MANUFACTURING CO.,INC.SANTA MARIACA93455RABOBANK, NATIONAL ASSOCIATIONCA331997-02-271997108261.00010NNNaT1997-09-30300000.00.0P I F0.0300000.0225000.00001
8991629995973006MARUTAMA HAWAII, INC.HONOLULUHI96830BANK OF HAWAIIHI01997-02-2719976061.00010NY2000-03-081997-03-3175000.00.0CHGOFF46383.075000.060000.01001
8991639996003010PACIFIC TRADEWINDS FAN & LIGHTKAILUAHI96734CENTRAL PACIFIC BANKHI01997-02-2719974810.00010NNNaT1997-05-3130000.00.0P I F0.030000.024000.00001